In this paper, a Probe Route based Defense Sinkhole Attack (PRDSA) scheme is proposed to resist Sinkhole attack and guarantee security for IoT, which is the first work that can detect, bypass and locate the sinkhole at the same time. The PRDSA scheme proposes a routing mechanism combining the far-sink reverse routing, equal-hop routing, and minimum hop routing, which can effectively circumvent the sinkhole attacks and find a safe route to the real sink, so that the scheme can achieve better sinkhole detection. More importantly, the PRDSA scheme overcomes the limitation of previous schemes that they cannot locate the sinkhole. During the detection of the sinkhole attack, the PRDSA scheme requires the nodes and the sink node to return the signature of the information (e.g., IDs, etc.), so that the location of the sinkhole can be determined. Furthermore, the PRDAS scheme mainly utilizes the characteristics of network energy consumption. The probe route of sinkhole attack mainly occurs in the region where the remaining energy exists. Thus, the PRDSA scheme has little impact on the network lifetime. Theory and experiments show that this scheme can achieve better performance than existing schemes in term of network security and lifetime.

Industrial Communication Systems (ICSs) have become an important part of many automated applications. Smart sensors for industrial communication systems have attracted much attention. Using smart sensors for industrial communication systems can allow the surrounding world to connect with the production equipment to reduce human resource requirements. However, due to their operating nature, smart sensors are often unattended and prone to different kinds of attacks. An on-off attack is one behavior that can cause serious damage to wireless sensor networks (WSNs) in industrial communication systems. To ensure network security, a trust joint light probe-based defense (TLPD) mechanism is proposed in which each smart sensor node selects a smart sensor node with high trust as the next hop to improve network security. The TLPD scheme is constituted by three major components, including Light probe routing, Trust Estimation and Trust-aware routing. Light probe routing is adopted for increasing the operation times of malicious nodes, trust evaluation scheme evaluates nodes trust according to operation behavior of nodes. Thus, the discrimination degree of node trust between temporary errors and disguised malicious behaviors can be increased compared to existing trust evaluation schemes. Thus, a malicious smart sensor node can be quickly and easily identified. The analytical results of the TLPD mechanism assessed is with a simulation experiment.

For the cloud computing based on Software Defined Networks (SDN), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer services response time due to the capacity-limited internet. To solve this problem, a novel services orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and services response delay. In SODA, the network is divided into three layers. (1) Data centers layer (DCL). Data centers release software with specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce services response delay. (2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along same routing path is higher to reduce redundancy data. (3) Vehicle network layer (VNL), mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation are conducted. The results illustrate that the proposed scheme has better performance compared with traditional scheme.

In this paper, unmanned aerial vehicles (UAVs) are used to efficiently collect information in an areas of interest. Based on the matrix completion, an optimal UAV data collection trajectory (OUDCT) scheme is proposed for improving energy efficiency and reducing redundant data by optimizing the trajectory of the UAV. With the proposed scheme, the backbone sampling points can be selected as follows. First, sampling points with higher degrees are selected as dominator sampling points. Second, sampling points with lower degrees are selected as virtual dominator sampling points to ensure that the information in all rows and columns is collected. Third, sampling points with lower degrees are selected as follower sampling points until the total number of selected sampling points satisfies the minimum requirement of the matrix completion. Thus, all the information in the monitoring area can be recovered by using the matrix completion. Finally, the optimal simulated annealing algorithm is used to plan the path of UAV based on the selected sampling points. The experimental results indicate that the performance of the OUDCT scheme is better than those in previous studies. Extensive simulation results are provided, which demonstrate that the OUDCT scheme can reduce data redundancy by 50-52% and increase the lifetime by 17% compared with the random selection sampling points scheme (RSSS).

With the rapid development of artificial intelligence (AI) technology, social networks (SNs) can use AI to extract useful knowledge of users to improve the quality of people's lives. However, AI faces many challenges for collecting the larger amount of data, such as larger data redundancy and higher energy consumption. To address these problems, a matrix completion-based Sampling Points Selection joint Intelligent Unmanned Aerial Vehicle (UAVs) Trajectory Optimization (SPS-IUTO) scheme for data acquisition is proposed. In terms of space, for one column, the probability that a sample point is selected is inversely proportional to the number of sample points selected by all previous rows. In terms of time, the first step is that sampling points with higher degree are selected as dominator sampling points in each row and column. The second step is that sampling points with lower degree are selected as virtual dominator sampling points. The movement trajectory of the UAVs is optimized using the proposed algorithm. As shown in the experimental results, the proposed scheme can achieve significant improvement in terms of energy and redundant data. The proposed SPS-JUTO scheme has the potential to improve the data collection for crowdsensing, thus promoting social networks to address societal and scien